Rapid sampling of molecular motions with prior information constraints

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways Using RRT* and MC

To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence.

Structural Organization and Dynamics of Homodimeric Cytohesin Family Arf GTPase Exchange Factors in Solution and on Membranes

Membrane dynamic processes require Arf GTPase activation by guanine nucleotide exchange factors (GEFs) with a Sec7 domain. Cytohesin family Arf GEFs function in signaling and cell migration through Arf GTPase activation on the plasma membrane and endosomes. In this study, the structural organization of two cytohesins (Grp1 and ARNO) was investigated in solution by size exclusion-small angle X-ray scattering and negative stain-electron microscopy and on membranes by dynamic light scattering, hydrogen-deuterium exchange-mass spectrometry and guanosine diphosphate (GDP)/guanosine triphosphate (GTP) exchange assays. The results suggest that cytohesins form elongated dimers with a central coiled coil and membrane-binding pleckstrin-homology (PH) domains at opposite ends. The dimers display significant conformational heterogeneity, with a preference for compact to intermediate conformations. Phosphoinositide-dependent membrane recruitment is mediated by one PH domain at a time and alters the conformational dynamics to prime allosteric activation by Arf-GTP. A structural model for membrane targeting and allosteric activation of full-length cytohesin dimers is discussed.

ART-RRT: As-Rigid-As-Possible search for protein conformational transition paths

The possible functions of a protein are strongly related to its structural rearrangements in the presence of other molecules or environmental changes. Hence, the evaluation of transition paths of proteins, which encodes conformational changes between stable states, is important since it may reveal the underlying mechanisms of the biochemical processes related to these motions. During the last few decades, different geometry-based methods have been proposed to predict such transition paths. However, in the cases where the solution requires complex motions, these methods, which typically constrain only locally the molecular structures, could produce physically irrelevant solutions involving self-intersection. Recently, we have proposed ART-RRT, an efficient method for finding ligand-unbinding pathways. It relies on the exploration of energy valleys in low-dimensional spaces, taking advantage of some mechanisms inspired from computer graphics to ensure the consistency of molecular structures. This article extends ART-RRT to the problem of finding probable conformational transition between two stable states for proteins. It relies on a bidirectional exploration rooted on the two end states and introduces an original strategy to attempt connections between the explored regions. The resulting method is able to produce at low computational cost biologically realistic paths free from self-intersection. These paths can serve as valuable input to other advanced methods for the study of proteins. A better understanding of conformational changes of proteins is important since it may reveal the underlying mechanisms of the biochemical processes related to such motions. Recently, the ART-RRT method has been introduced for finding ligand-unbinding pathways. This article presents an adaptation of the method for finding probable conformational transition between two stable states of a protein. The method is not only computationally cost-effective but also able to produce biologically realistic paths which are free from self-intersection.

Structure-Guided Protein Transition Modeling with a Probabilistic Roadmap Algorithm

Proteins are macromolecules in perpetual motion, switching between structural states to modulate their function. A detailed characterization of the precise yet complex relationship between protein structure, dynamics, and function requires elucidating transitions between functionally-relevant states. Doing so challenges both wet and dry laboratories, as protein dynamics involves disparate temporal scales. In this paper, we present a novel, sampling-based algorithm to compute transition paths. The algorithm exploits two main ideas. First, it leverages known structures to initialize its search and define a reduced conformation space for rapid sampling. This is key to address the insufficient sampling issue suffered by sampling-based algorithms. Second, the algorithm embeds samples in a nearest-neighbor graph where transition paths can be efficiently computed via queries. The algorithm adapts the probabilistic roadmap framework that is popular in robot motion planning. In addition to efficiently computing lowest-cost paths between any given structures, the algorithm allows investigating hypotheses regarding the order of experimentally-known structures in a transition event. This novel contribution is likely to open up new venues of research. Detailed analysis is presented on multiple-basin proteins of relevance to human disease. Multiscaling and the AMBER ff14SB force field are used to obtain energetically-credible paths at atomistic detail.

Using Spectral Representation to Classify Proteins' Conformational States

Numerous proteins are molecular targets for drug action and hence are important in drug discovery. Structure-based computational drug discovery relies on detailed information regarding protein conformations for subsequent drug screening in silico. There are two key issues in analyzing protein conformations in virtual screening. The first considers the protein’s conformational change in response to physical and chemical conditions. The second is the protein’s atomic resolution reconstruction from X-ray crystallography or nuclear magnetic resonance (NMR) data. In this latter problem, information is needed regarding the sample’s position relative to the source of X-rays. Here, we introduce a new measure for classifying protein conformational states using spectral representation and Wigner’s D-functions. Predictions based on the new measure are in good agreement with conformational states of proteins. These results could also be applied to improve conformational alignment of the snapshots given by protein crystallography.

Collision-free poisson motion planning in ultra high-dimensional molecular conformation spaces

The function of protein, RNA, and DNA is modulated by fast, dynamic exchanges between three-dimensional conformations. Conformational sampling of biomolecules with exact and nullspace inverse kinematics, using rotatable bonds as revolute joints and noncovalent interactions as holonomic constraints, can accurately characterize these native ensembles. However, sampling biomolecules remains challenging owing to their ultra-high dimensional configuration spaces, and the requirement to avoid (self-) collisions, which results in low acceptance rates. Here, we present two novel mechanisms to overcome these limitations. First, we introduce temporary constraints between near-colliding links. The resulting constraint varieties instantaneously redirect the search for collision-free conformations, and couple motions between distant parts of the linkage. Second, we adapt a randomized Poisson-disk motion planner, which prevents local oversampling and widens the search, to ultra-high dimensions. Tests on several model systems show that the sampling acceptance rate can increase from 16% to 70%, and that the conformational coverage in loop modeling measured as average closeness to existing loop conformations doubled. Correlated protein motions identified with our algorithm agree with those from MD simulations. © 2018 Wiley Periodicals, Inc.

Structures of monomeric and oligomeric forms of the Toxoplasma gondii perforin-like protein 1

Toxoplasma and Plasmodium are the parasitic agents of toxoplasmosis and malaria, respectively, and use perforin-like proteins (PLPs) to invade host organisms and complete their life cycles. The Toxoplasma gondii PLP1 (TgPLP1) is required for efficient exit from parasitophorous vacuoles in which proliferation occurs. We report structures of the membrane attack complex/perforin (MACPF) and Apicomplexan PLP C-terminal β-pleated sheet (APCβ) domains of TgPLP1. The MACPF domain forms hexameric assemblies, with ring and helix geometries, and the APCβ domain has a novel β-prism fold joined to the MACPF domain by a short linker. Molecular dynamics simulations suggest that the helical MACPF oligomer preserves a biologically important interface, whereas the APCβ domain binds preferentially through a hydrophobic loop to membrane phosphatidylethanolamine, enhanced by the additional presence of inositol phosphate lipids. This mode of membrane binding is supported by site-directed mutagenesis data from a liposome-based assay. Together, these structural and biophysical findings provide insights into the molecular mechanism of membrane targeting by TgPLP1.

Maintaining and Enhancing Diversity of Sampled Protein Conformations in Robotics-Inspired Methods

The ability to efficiently sample structurally diverse protein conformations allows one to gain a high-level view of a protein's energy landscape. Algorithms from robot motion planning have been used for conformational sampling, and several of these algorithms promote diversity by keeping track of "coverage" in conformational space based on the local sampling density. However, large proteins present special challenges. In particular, larger systems require running many concurrent instances of these algorithms, but these algorithms can quickly become memory intensive because they typically keep previously sampled conformations in memory to maintain coverage estimates. In addition, robotics-inspired algorithms depend on defining useful perturbation strategies for exploring the conformational space, which is a difficult task for large proteins because such systems are typically more constrained and exhibit complex motions. In this article, we introduce two methodologies for maintaining and enhancing diversity in robotics-inspired conformational sampling. The first method addresses algorithms based on coverage estimates and leverages the use of a low-dimensional projection to define a global coverage grid that maintains coverage across concurrent runs of sampling. The second method is an automatic definition of a perturbation strategy through readily available flexibility information derived from B-factors, secondary structure, and rigidity analysis. Our results show a significant increase in the diversity of the conformations sampled for proteins consisting of up to 500 residues when applied to a specific robotics-inspired algorithm for conformational sampling. The methodologies presented in this article may be vital components for the scalability of robotics-inspired approaches.

Structural Dynamics Control Allosteric Activation of Cytohesin Family Arf GTPase Exchange Factors

Membrane dynamic processes including vesicle biogenesis depend on Arf GTPase activation by guanine nucleotide exchange factors (GEFs) containing a catalytic Sec7 domain and a membrane targeting module such as a PH domain. The catalytic output of cytohesin family Arf GEFs is controlled by autoinhibitory interactions that impede accessibility of the exchange site in the Sec7 domain. These restraints can be relieved through activator Arf-GTP binding to an allosteric site comprising the PH domain and proximal autoinhibitory elements (Sec7-PH linker and C-terminal helix). Small angle X-ray scattering and negative-stain electron microscopy were used to investigate the structural organization and conformational dynamics of Cytohesin-3 (Grp1) in autoinhibited and active states. The results support a model in which hinge dynamics in the autoinhibited state expose the activator site for Arf-GTP binding, while subsequent C-terminal helix unlatching and repositioning unleash conformational entropy in the Sec7-PH linker to drive exposure of the exchange site.

Detecting intermediate protein conformations using algebraic topology

Background

Understanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima.

Results

We present a hierarchical clustering and algebraic topology based method that detects regions of interest in protein conformational space. The method is based on several techniques. We use coarse grained protein conformational search, efficient robust dimensionality reduction and topological analysis via persistent homology as the main tools. We use two dimensionality reduction methods as well, robust Principal Component Analysis (PCA) and Isomap, to generate a reduced representation of the data while preserving most of the variance in the data.

Conclusions

Our hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.

Structural Insight into the 14-3-3 Protein-dependent Inhibition of Protein Kinase ASK1 (Apoptosis Signal-regulating kinase 1)

Apoptosis signal-regulating kinase 1 (ASK1, also known as MAP3K5), a member of the mitogen-activated protein kinase kinase kinase (MAP3K) family, regulates diverse physiological processes. The activity of ASK1 is triggered by various stress stimuli and is involved in the pathogenesis of cancer, neurodegeneration, inflammation, and diabetes. ASK1 forms a high molecular mass complex whose activity is, under non-stress conditions, suppressed through interaction with thioredoxin and the scaffolding protein 14-3-3. The 14-3-3 protein binds to the phosphorylated Ser-966 motif downstream of the ASK1 kinase domain. The role of 14-3-3 in the inhibition of ASK1 has yet to be elucidated. In this study we performed structural analysis of the complex between the ASK1 kinase domain phosphorylated at Ser-966 (pASK1-CD) and the 14-3-3ζ protein. Small angle x-ray scattering (SAXS) measurements and chemical cross-linking revealed that the pASK1-CD·14-3-3ζ complex is dynamic and conformationally heterogeneous. In addition, structural analysis coupled with the results of phosphorus NMR and time-resolved tryptophan fluorescence measurements suggest that 14-3-3ζ interacts with the kinase domain of ASK1 in close proximity to its active site, thus indicating this interaction might block its accessibility and/or affect its conformation.

Prion Protein-Antibody Complexes Characterized by Chromatography-Coupled Small-Angle X-Ray Scattering

Aberrant self-assembly, induced by structural misfolding of the prion proteins, leads to a number of neurodegenerative disorders. In particular, misfolding of the mostly α-helical cellular prion protein (PrP(C)) into a β-sheet-rich disease-causing isoform (PrP(Sc)) is the key molecular event in the formation of PrP(Sc) aggregates. The molecular mechanisms underlying the PrP(C)-to-PrP(Sc) conversion and subsequent aggregation remain to be elucidated. However, in persistently prion-infected cell-culture models, it was shown that treatment with monoclonal antibodies against defined regions of the prion protein (PrP) led to the clearing of PrP(Sc) in cultured cells. To gain more insight into this process, we characterized PrP-antibody complexes in solution using a fast protein liquid chromatography coupled with small-angle x-ray scattering (FPLC-SAXS) procedure. High-quality SAXS data were collected for full-length recombinant mouse PrP [denoted recPrP(23-230)] and N-terminally truncated recPrP(89-230), as well as their complexes with each of two Fab fragments (HuM-P and HuM-R1), which recognize N- and C-terminal epitopes of PrP, respectively. In-line measurements by fast protein liquid chromatography coupled with SAXS minimized data artifacts caused by a non-monodispersed sample, allowing structural analysis of PrP alone and in complex with Fab antibodies. The resulting structural models suggest two mechanisms for how these Fabs may prevent the conversion of PrP(C) into PrP(Sc).

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA was applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.

Many proteins are nanomachines that perform mechanical or chemical work by changing their three-dimensional shape and cycle between multiple conformational states. Computer simulations of such conformational transitions provide mechanistic insights into protein function but such simulations have been challenging. In particular, it is not clear how to quantitatively compare current simulation methods or to assess their accuracy. To that end, we present a general and flexible computational framework for quantifying transition paths—by measuring mutual geometric similarity—that, compared with existing approaches, requires minimal a-priori assumptions and can take advantage of full atomic detail alongside heuristic information derived from intuition. Using our Path Similarity Analysis (PSA) framework in parallel with several existing quantitative approaches, we examine transitions generated for a toy model of a transition and two biological systems, the enzyme adenylate kinase and diphtheria toxin. Our results show that PSA enables the quantitative comparison of different path sampling methods and aids the identification of potentially important atomistic motions by exploiting geometric information in transition paths. The method has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing macromolecular conformational transitions.

Insights into mechanism kinematics for protein motion simulation

Background

The high demanding computational requirements necessary to carry out protein motion simulations make it difficult to obtain information related to protein motion. On the one hand, molecular dynamics simulation requires huge computational resources to achieve satisfactory motion simulations. On the other hand, less accurate procedures such as interpolation methods, do not generate realistic morphs from the kinematic point of view. Analyzing a protein’s movement is very similar to serial robots; thus, it is possible to treat the protein chain as a serial mechanism composed of rotational degrees of freedom. Recently, based on this hypothesis, new methodologies have arisen, based on mechanism and robot kinematics, to simulate protein motion. Probabilistic roadmap method, which discretizes the protein configurational space against a scoring function, or the kinetostatic compliance method that minimizes the torques that appear in bonds, aim to simulate protein motion with a reduced computational cost.

Results

In this paper a new viewpoint for protein motion simulation, based on mechanism kinematics is presented. The paper describes a set of methodologies, combining different techniques such as structure normalization normalization processes, simulation algorithms and secondary structure detection procedures. The combination of all these procedures allows to obtain kinematic morphs of proteins achieving a very good computational cost-error rate, while maintaining the biological meaning of the obtained structures and the kinematic viability of the obtained motion.

Conclusions

The procedure presented in this paper, implements different modules to perform the simulation of the conformational change suffered by a protein when exerting its function. The combination of a main simulation procedure assisted by a secondary structure process, and a side chain orientation strategy, allows to obtain a fast and reliable simulations of protein motion.

Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods

BACKGROUND

Obtaining atomic-scale information about large-amplitude conformational transitions in proteins is a challenging problem for both experimental and computational methods. Such information is, however, important for understanding the mechanisms of interaction of many proteins.

METHODS

This paper presents a computationally efficient approach, combining methods originating from robotics and computational biophysics, to model protein conformational transitions. The ability of normal mode analysis to predict directions of collective, large-amplitude motions is applied to bias the conformational exploration performed by a motion planning algorithm. To reduce the dimension of the problem, normal modes are computed for a coarse-grained elastic network model built on short fragments of three residues. Nevertheless, the validity of intermediate conformations is checked using the all-atom model, which is accurately reconstructed from the coarse-grained one using closed-form inverse kinematics.

RESULTS

Tests on a set of ten proteins demonstrate the ability of the method to model conformational transitions of proteins within a few hours of computing time on a single processor. These results also show that the computing time scales linearly with the protein size, independently of the protein topology. Further experiments on adenylate kinase show that main features of the transition between the open and closed conformations of this protein are well captured in the computed path.

CONCLUSIONS

The proposed method enables the simulation of large-amplitude conformational transitions in proteins using very few computational resources. The resulting paths are a first approximation that can directly provide important information on the molecular mechanisms involved in the conformational transition. This approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods.

SIMS: a hybrid method for rapid conformational analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of “active” residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems.

MORPH-PRO: a novel algorithm and web server for protein morphing

Background

Proteins are known to be dynamic in nature, changing from one conformation to another while performing vital cellular tasks. It is important to understand these movements in order to better understand protein function. At the same time, experimental techniques provide us with only single snapshots of the whole ensemble of available conformations. Computational protein morphing provides a visualization of a protein structure transitioning from one conformation to another by producing a series of intermediate conformations.

Results

We present a novel, efficient morphing algorithm, Morph-Pro based on linear interpolation. We also show that apart from visualization, morphing can be used to provide plausible intermediate structures. We test this by using the intermediate structures of a c-Jun N-terminal kinase (JNK1) conformational change in a virtual docking experiment. The structures are shown to dock with higher score to known JNK1-binding ligands than structures solved using X-Ray crystallography. This experiment demonstrates the potential applications of the intermediate structures in modeling or virtual screening efforts.

Conclusions

Visualization of protein conformational changes is important for characterization of protein function. Furthermore, the intermediate structures produced by our algorithm are good approximations to true structures. We believe there is great potential for these computationally predicted structures in protein-ligand docking experiments and virtual screening. The Morph-Pro web server can be accessed at http://morph-pro.bioinf.spbau.ru.

Structural intermediates in a model of the substrate translocation path of the bacterial glutamate transporter homologue GltPh

Excitatory amino acid transporters (EAATs) are membrane proteins responsible for reuptake of glutamate from the synaptic cleft to terminate neurotransmission and help prevent neurotoxically high, extracellular glutamate concentrations. Important structural information about these proteins emerged from crystal structures of GltPh, a bacterial homologue of EAATs, in conformations facing outward and inward. These remarkably different conformations are considered to be end points of the substrate translocation path (STP), suggesting that the transport mechanism involves major conformational rearrangements that remain uncharted. To investigate possible steps in the structural transitions of the STP between the two end-point conformations, we applied a combination of computational modeling methods (motion planning, molecular dynamics simulations, and mixed elastic network models). We found that the conformational changes in the transition involve mainly the repositioning the "transport domain" and the "trimerization domain" identified previously in the crystal structures. The two domains move in opposite directions along the membrane normal, and the transport domain also tilts by ∼17° with respect to this axis. Moreover, the TM3-4 loop undergoes a flexible, "restraining bar"-like conformational change with respect to the transport domain. As a consequence of these conformational rearrangements along the transition path we calculated a significant decrease of nearly 20% in the area of the transport-to-trimerization domain interface (TTDI). Water penetrates parts of the TTDI in the modeled intermediates but very much less in the end-point conformations. We show that these characteristics of the modeled intermediate states agree with experimental results from residue-accessibility studies in individual monomers and identify specific residues that can be used to test the proposed STP. Moreover, MD simulations of complete GltPh trimers constructed from initially identical monomer intermediates suggest that asymmetry can appear in the trimer, consonant with available experimental data showing independent transport kinetics by individual monomers in the trimers.

Rigid-CLL: avoiding constant-distance computations in cell linked-lists algorithms

Many of the existing molecular simulation tools require the efficient identification of the set of nonbonded interacting atoms. This is necessary, for instance, to compute the energy values or the steric contacts between atoms. Cell linked-lists can be used to determine the pairs of atoms closer than a given cutoff distance in asymptotically optimal time. Despite this long-term optimality, many spurious distances are anyway computed with this method. Therefore, several improvements have been proposed, most of them aiming to refine the volume of influence for each atom. Here, we suggest a different improvement strategy based on avoiding to fill cells with those atoms that are always at a constant distance of a given atom. This technique is particularly effective when large groups of the particles in the simulation behave as rigid bodies as it is the case in simplified models considering only few of the degrees of freedom of the molecule. In these cases, the proposed technique can reduce the number of distance computations by more than one order of magnitude, as compared with the standard cell linked-list technique. The benefits of this technique are obtained without incurring in additional computation costs, because it carries out the same operations as the standard cell linked-list algorithm, although in a different order. Since the focus of the technique is the order of the operations, it might be combined with existing improvements based on bounding the volume of influence for each atom.

Detection and alignment of 3D domain swapping proteins using angle-distance image-based secondary structural matching techniques

This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had “opened” their “closed” structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimer's and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8Å for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications.

Markov dynamic models for long-timescale protein motion

Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.

Tracing conformational changes in proteins

Background

Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions.

Results

In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on four well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions.

Conclusions

The proposed method effectively captures large-scale conformational changes and produces pathways that are consistent with experimental data and other computational studies. The method represents an important first step towards a larger scale modeling of more complex biological systems.

Control of lipase enantioselectivity by engineering the substrate binding site and access channel

Lipase from Burkholderia cepacia (BCL) has proven to be a very useful biocatalyst for the resolution of 2-substituted racemic acid derivatives, which are important chiral building blocks. Our previous work showed that enantioselectivity of the wild-type BCL could be improved by chemical engineering of the substrate's molecular structure. From this earlier study, three amino acids (L17, V266 and L287) were proposed as targets for mutagenesis aimed at tailoring enzyme enantioselectivity. In the present work, a small library of 57 BCL single mutants targeted on these three residues was constructed and screened for enantioselectivity towards (R,S)-2-chloro ethyl 2-bromophenylacetate. This led to the fast isolation of three single mutants with a remarkable tenfold enhanced or reversed enantioselectivity. Analysis of substrate docking and access trajectories in the active site was then performed. From this analysis, the construction of 13 double mutants was proposed. Among them, an outstanding improved mutant of BCL was isolated that showed an E value of 178 and a 15-fold enhanced specific activity compared to the parental enzyme; thus, this study demonstrates the efficiency of the semirational engineering strategy.